The present disclosure relates generally to machine learning systems, and more particularly, to a human action data set generator for a machine learning system.
Applying deep learning techniques to solve standard computer vision problems has inspired researchers to propose new computer vision problems in different domains. Training data plays a significant role in machine learning processes, especially deep learning approaches which are data hungry. Training data is used during machine learning processes to provide numerous images and/or videos to train computer systems to distinguish between subject matter, such as objects, actions, and/or scenes, within the images and/or videos. In order to solve new problems using machine learning processes, a large amount of training data is required, which may, in many cases, pose logistical difficulties. One method of generating human action data sets includes asking subjects to perform a series of actions in front of a camera. However, this method is not scalable, as the amount of required data for training data purposes far exceeds the number of available subjects and the time available to perform the actions. Another method of generating human action data sets includes retrieving existing videos having labels that identify the actions performed in the videos. However, this method is limited based on the number of existing videos that have labels according to the needs of a required training data set.
Accordingly, more efficient systems and methods for obtaining data sets are needed in order to satisfy data requirements of machine learning processes.